Cell-cell interaction analysis: Wilks et al.
Yingxin Lin
Last compiled on 28 December, 2020
1 Data and package
library(scran)
library(SingleCellExperiment)
library(scater)
library(scattermore)
library(moon)
library(ggplot2)
library(ggthemes)
library(ggpubr)
library(reshape2)
library(dplyr)
library(stringr)
library(pheatmap)
library(CellChat)
library(RColorBrewer)
library(gridExtra)coldata_wilks <- readRDS("results/wilk_results/coldata_wilks.rds")
meta_wilks <- readRDS("results/wilk_results/meta_wilks.rds")# CCI results (Cellchat)
cellchat_res_list <- readRDS("results/wilk_results/wilk_cellchat_res_list.rds")rankNet_byCellType <- function(object, slot.name = "netP",
x.rotation = 90, title = NULL, color.use = NULL,
bar.w = 0.75, font.size = 8)
{
object1 <- methods::slot(object, slot.name)
prob1 = object1$prob
df <- melt(apply(prob1, 3, function(x) {
df <- melt(x)
colnames(df) <- c("Ligand", "Receptor", "value")
df
}))
df <- df[, c("Ligand", "Receptor", "L1", "value")]
colnames(df)[3] <- "Pathway"
return(df)
}2 Overall pattern analysis
rankNet_byCellType_list <- lapply(cellchat_res_list, rankNet_byCellType)
rankNet_byCellType_list <- melt(rankNet_byCellType_list)
rankNet_byCellType_list$Ligand_group <- unlist(lapply(strsplit(as.character(rankNet_byCellType_list$Ligand),
"_"), "[[", 1))
rankNet_byCellType_list$Receptor_group <- unlist(lapply(strsplit(as.character(rankNet_byCellType_list$Receptor),
"_"), "[[", 1))
saveRDS(rankNet_byCellType_list, file = "results/wilk_results/rankNet_byCellType_list_wilks.rds")severity_color <- c("#2ca02c", "#FFD92F", "#7570B3")
names(severity_color) <- c("Healthy", "Moderate", "Severe")rankNet_byGroup_agg <- aggregate(rankNet_byCellType_list$value,
list(rankNet_byCellType_list$Ligand_group,
rankNet_byCellType_list$Receptor_group,
rankNet_byCellType_list$L1,
rankNet_byCellType_list$Pathway),
sum)
colnames(rankNet_byGroup_agg) <- c("Ligand_group",
"Receptor_group",
"sample",
"Pathway",
"value")
features <- paste(rankNet_byGroup_agg$Ligand_group,
rankNet_byGroup_agg$Receptor_group,
rankNet_byGroup_agg$Pathway, sep = "_")
rankNet_byGroup_agg$features <- featuresrankNet_byGroup_agg_all <- dcast2(rankNet_byGroup_agg,
features ~ sample,
fun.aggregate = sum, value.var = "value")
rankNet_byGroup_agg_all <- rankNet_byGroup_agg_all[rowSums(rankNet_byGroup_agg_all) > 0, ]
rankNet_byGroup_agg_all <- rankNet_byGroup_agg_all[rowSums(rankNet_byGroup_agg_all!=0) > 2, ]2.1 Feature selction: kruskal test
kruskal_pvalue <- list()
for (i in 1:nrow(rankNet_byGroup_agg_all)) {
# if (i %% 100 == 0) print(i)
kruskal_res <- try(kruskal.test(unlist(rankNet_byGroup_agg_all[i,]) ~ meta_wilks[colnames(rankNet_byGroup_agg_all), ]$Condition), silent = TRUE)
kruskal_pvalue[[i]] <- try(kruskal_res$p.value, silent = TRUE)
}
kruskal_pvalue <- lapply(kruskal_pvalue, function(x) {
if (class(x) == "try-error") {
x <- NULL
}
x
})
names(kruskal_pvalue) <- rownames(rankNet_byGroup_agg_all)
kruskal_pvalue <- unlist(kruskal_pvalue)
kruskal_pvalue <- p.adjust(kruskal_pvalue, method = "BH")
saveRDS(kruskal_pvalue, file = "results/wilk_results/CCI_kruskal_pvalue_condition_wilk.rds")2.2 PCA
pca_patient <- prcomp(t(-1/log(rankNet_byGroup_agg_all[names(kruskal_pvalue[kruskal_pvalue < 0.2]),])),
scale. = TRUE, center = TRUE)
library(ggrepel)
pca1 <- ggplot(data.frame(pca_patient$x), aes(x = pca_patient$x[, 1],
y = pca_patient$x[, 2],
color = meta_wilks[rownames(pca_patient$x),]$Condition)) +
geom_point(size = 4, alpha = 0.8) +
# geom_text_repel(aes(label = rownames(pca_patient$x))) +
theme_yx() +
theme(aspect.ratio = 1) +
scale_color_manual(values = severity_color) +
xlab("PCA1") +
ylab("PCA2") +
labs(color = "")
pca2 <- ggplot(data.frame(pca_patient$x), aes(x = pca_patient$x[, 1],
y = pca_patient$x[, 3],
color = meta_wilks[rownames(pca_patient$x),]$Condition)) +
geom_point(size = 3, alpha = 0.8) +
# geom_text_repel(aes(label = rownames(pca_patient$x))) +
theme_yx() +
theme(aspect.ratio = 1) +
scale_color_manual(values = severity_color) +
xlab("PCA1") +
ylab("PCA3") +
labs(color = "")
pca3 <- ggplot(data.frame(pca_patient$x), aes(x = pca_patient$x[, 2],
y = pca_patient$x[, 3],
color = meta_wilks[rownames(pca_patient$x),]$Condition)) +
geom_point(size = 3, alpha = 0.8) +
# geom_text_repel(aes(label = rownames(pca_patient$x))) +
theme_yx() +
theme(aspect.ratio = 1) +
scale_color_manual(values = severity_color) +
xlab("PCA2") +
ylab("PCA3") +
labs(color = "")
ggarrange(pca1, pca2, pca3, align = "hv",
common.legend = TRUE, ncol = 2, nrow = 2)
2.3 Aggregation by samples
aff_mat_bySample <- lapply(split(rankNet_byGroup_agg, rankNet_byGroup_agg$sample),
function(x) dcast2(x, Ligand_group~Receptor_group,
fun.aggregate = mean, value.var = "value"))
all_cellTypes <- names(table(coldata_wilks$cell.type.fine))
aff_mat_bySample <- lapply(aff_mat_bySample, function(x) {
mat <- matrix(0, ncol = length(all_cellTypes), nrow = length(all_cellTypes))
colnames(mat) <- rownames(mat) <- all_cellTypes
mat[rownames(x), colnames(x)] <- as.matrix(x)
mat
})
selected_cellTypes <- all_cellTypes[!all_cellTypes %in% c("intermediate", "unassigned",
"RBC", "Platelet", "SC & Eosinophil")]
aff_mat_bySample <- lapply(aff_mat_bySample, function(x) {
x <- x[selected_cellTypes, selected_cellTypes]
x
})
aff_mat_bySample <- lapply(aff_mat_bySample, function(x) {
(x - min(x))/(max(x) - min(x))
})
p <- lapply(1:length(aff_mat_bySample), function(i) {
pheatmap(aff_mat_bySample[[i]],
cluster_cols = FALSE,
cluster_rows = FALSE,
main = names(aff_mat_bySample)[i],
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100))
})
pdf("figures/WilkEtAl/cellchat_CCI_network_sample_byCellType.pdf",
width = 20, height = 20)
do.call(grid.arrange, list(grobs = lapply(p, function(x) x$gtable), ncol = 3))
dev.off()## quartz_off_screen
## 2#
severe_patients <- rownames(meta_wilks)[meta_wilks$Condition == "Severe"]
aff_mat_severe <- Reduce("+", aff_mat_bySample[names(aff_mat_bySample) %in% severe_patients])/length(severe_patients)
moderate_patients <- rownames(meta_wilks)[meta_wilks$Condition == "Moderate"]
aff_mat_moderate <- Reduce("+", aff_mat_bySample[names(aff_mat_bySample) %in% moderate_patients])/length(moderate_patients)
control_patients <- rownames(meta_wilks)[meta_wilks$Condition == "Healthy"]
aff_mat_control <- Reduce("+", aff_mat_bySample[names(aff_mat_bySample) %in% control_patients])/length(control_patients)
p_severe <- pheatmap(aff_mat_severe, cluster_cols = FALSE,
cluster_rows = FALSE,
main = "severe (average across samples)",
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100),
breaks = seq(0, max(aff_mat_severe), max(aff_mat_severe)/100))
library(RColorBrewer)
p_moderate <- pheatmap(aff_mat_moderate,
cluster_cols = FALSE,
cluster_rows = FALSE,
main = "moderate (average across samples)",
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100),
breaks = seq(0, max(aff_mat_moderate), max(aff_mat_moderate)/100))
p_control <- pheatmap(aff_mat_control,
cluster_cols = FALSE,
cluster_rows = FALSE,
main = "control (average across samples)",
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100),
breaks = seq(0, max(aff_mat_control), max(aff_mat_control)/100))
pdf("figures/WilkEtAl/cellchat_CCI_network_byCondition_noScale.pdf",
width = 18, height = 6)
do.call(grid.arrange, list(grobs = list(p_control$gtable,
p_moderate$gtable,
p_severe$gtable), ncol = 3))
dev.off()## quartz_off_screen
## 2aff_mat_diff <- aff_mat_severe - aff_mat_moderate
pheatmap(aff_mat_diff,
cluster_cols = FALSE,
cluster_rows = FALSE,
color = colorRampPalette(c("blue", "white", "red"))(100),
breaks = c(seq(min(aff_mat_diff), 0, (0 - min(aff_mat_diff))/50),
seq(0.01, max(aff_mat_diff), (max(aff_mat_diff))/50)),
main = "server - moderate (wilks et al.)",
#file = "figures/WilkEtAl/cellchat_CCI_network_byCondition_diff_severe.pdf",
width = 8,
height = 7)
aff_mat_diff <- aff_mat_moderate - aff_mat_control
pheatmap(aff_mat_diff,
cluster_cols = FALSE,
cluster_rows = FALSE,
color = colorRampPalette(c("blue", "white", "red"))(100),
breaks = c(seq(min(aff_mat_diff), 0, (0 - min(aff_mat_diff))/50),
seq(0.01, max(aff_mat_diff), (max(aff_mat_diff))/50)),
main = "moderate - control (wilks et al.)",
#file = "figures/WilkEtAl/cellchat_CCI_network_byCondition_diff_moderate.pdf",
width = 8,
height = 7)
aff_mat_diff <- aff_mat_severe - aff_mat_control
pheatmap(aff_mat_diff,
cluster_cols = FALSE,
cluster_rows = FALSE,
color = colorRampPalette(c("blue", "white", "red"))(100),
breaks = c(seq(min(aff_mat_diff), 0, (0 - min(aff_mat_diff))/50),
seq(0.01, max(aff_mat_diff), (max(aff_mat_diff))/50)),
main = "severe - control (wilks et al.)",
#file = "figures/WilkEtAl/cellchat_CCI_network_byCondition_diff_severe_control.pdf",
width = 8,
height = 7)
cellType_col <- moon_pal(22)[-c(1:2)][c(20, 1:19)]
cellType_col <- append(cellType_col, c("grey80", "grey30"))
names(cellType_col) <- c(names(table(coldata_wilks$cell.type)), c("intermediate", "unassigned"))
saveRDS(cellType_col, file = "results/cellType_col")mat <- aff_mat_control
cci_control <- melt(as.matrix(mat))
colnames(cci_control) <- c("Ligand", "Receptor", "n")
library(igraph)
library(ggraph)
g <- graph_from_data_frame(data.frame(cci_control))
E(g)$weights <- ifelse(cci_control$n == 0,
1e-10, abs(cci_control$n))
V(g)$color <- cellType_col[V(g)$name]
# pdf("figures/WilkEtAl/cellchat_CCI_network_byCondition_Control_network.pdf",
# width = 8,
# height = 6)
plot(g,
vertex.size = 20,
vertex.color = V(g)$color,
vertex.label.color = "black",
vertex.label.cex = 1,
edge.width = E(g)$weights * 20,
edge.arrow.size = log(1/E(g)$weights)/80,
#edge.arrow.size = 0.01,
#edge.color = E(g)$sign,
edge.curved = 0.3,
layout = layout_in_circle,
main = "Control")
## [1] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [19] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [37] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [55] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [73] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [91] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [109] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [127] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [145] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [163] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [181] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [199] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [217] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [235] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [253] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [271] 0.3 0.3# dev.off()mat <- aff_mat_moderate
cci_moderate <- melt(as.matrix(mat))
colnames(cci_moderate) <- c("Ligand", "Receptor", "n")
library(igraph)
library(ggraph)
g <- graph_from_data_frame(data.frame(cci_moderate))
E(g)$weights <- ifelse(cci_moderate$n == 0,
1e-10, abs(cci_moderate$n))
#E(g)$sign <- ifelse(sign(cci_moderate$n) == 1, "#d62728", "#1f77b4")
g <- delete_edges(g, E(g)[cci_moderate$n == 0])
V(g)$color <- cellType_col[V(g)$name]
# pdf("figures/WilkEtAl/cellchat_CCI_network_byCondition_moderate_network.pdf",
# width = 8,
# height = 6)
plot(g,
vertex.size = 20,
vertex.color = V(g)$color,
vertex.label.color = "black",
vertex.label.cex = 1,
edge.width = E(g)$weights * 20,
edge.arrow.size = log(1/E(g)$weights)/20,
#edge.arrow.size = 0.01,
#edge.color = E(g)$sign,
edge.curved = 0.3,
layout = layout_in_circle,
main = "moderate")
## [1] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [19] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [37] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [55] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [73] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [91] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [109] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [127] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [145] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3# dev.off()mat <- aff_mat_severe
cci_severe <- melt(as.matrix(mat))
colnames(cci_severe) <- c("Ligand", "Receptor", "n")
library(igraph)
library(ggraph)
g <- graph_from_data_frame(data.frame(cci_severe))
E(g)$weights <- ifelse(cci_severe$n == 0,
1e-10, abs(cci_severe$n))
g <- delete_edges(g, E(g)[cci_severe$n == 0])
V(g)$color <- cellType_col[V(g)$name]
# pdf("figures/WilkEtAl/cellchat_CCI_network_byCondition_severe_network.pdf",
# width = 8,
# height = 6)
plot(g,
vertex.size = 20,
vertex.color = V(g)$color,
vertex.label.color = "black",
vertex.label.cex = 1,
edge.width = E(g)$weights * 20,
edge.arrow.size = log(1/E(g)$weights)/20,
#edge.arrow.size = 0.01,
#edge.color = E(g)$sign,
edge.curved = 0.3,
layout = layout_in_circle,
main = "severe")
## [1] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [19] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [37] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [55] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [73] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [91] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [109] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [127] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [145] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [163] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [181] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [199] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [217] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [235] 0.3 0.3 0.3 0.3# dev.off()3 Session Info
sessionInfo()## R version 4.0.2 RC (2020-06-20 r78727)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ggraph_2.0.3 igraph_1.1.0
## [3] ggrepel_0.8.2 gridExtra_2.3
## [5] RColorBrewer_1.1-2 CellChat_0.0.1
## [7] bigmemory_4.5.36 pheatmap_1.0.12
## [9] stringr_1.4.0 dplyr_1.0.2
## [11] reshape2_1.4.4 ggpubr_0.3.0
## [13] ggthemes_4.2.0 moon_0.1.0
## [15] scattermore_0.6 scater_1.16.1
## [17] ggplot2_3.3.2 scran_1.16.0
## [19] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.1
## [21] DelayedArray_0.14.0 matrixStats_0.56.0
## [23] Biobase_2.48.0 GenomicRanges_1.40.0
## [25] GenomeInfoDb_1.24.2 IRanges_2.22.2
## [27] S4Vectors_0.26.1 BiocGenerics_0.34.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.8
## [3] circlize_0.4.10 systemfonts_0.2.3
## [5] NMF_0.30.1 plyr_1.8.6
## [7] BiocParallel_1.22.0 listenv_0.8.0
## [9] gridBase_0.4-7 digest_0.6.25
## [11] foreach_1.5.0 htmltools_0.5.0
## [13] viridis_0.5.1 ggalluvial_0.12.0
## [15] magrittr_1.5 cluster_2.1.0
## [17] doParallel_1.0.15 openxlsx_4.1.5
## [19] limma_3.44.3 graphlayouts_0.7.0
## [21] sna_2.5 ComplexHeatmap_2.4.2
## [23] globals_0.12.5 svglite_1.2.3.2
## [25] colorspace_1.4-1 haven_2.3.1
## [27] xfun_0.18 crayon_1.3.4
## [29] RCurl_1.98-1.2 jsonlite_1.6.1
## [31] bigmemory.sri_0.1.3 iterators_1.0.12
## [33] glue_1.4.1 polyclip_1.10-0
## [35] pals_1.6 registry_0.5-1
## [37] gtable_0.3.0 zlibbioc_1.34.0
## [39] XVector_0.28.0 GetoptLong_1.0.0
## [41] car_3.0-8 BiocSingular_1.4.0
## [43] future.apply_1.5.0 shape_1.4.4
## [45] maps_3.3.0 abind_1.4-5
## [47] scales_1.1.1 edgeR_3.30.3
## [49] rngtools_1.5 bibtex_0.4.2.2
## [51] rstatix_0.6.0 Rcpp_1.0.4.6
## [53] viridisLite_0.3.0 xtable_1.8-4
## [55] clue_0.3-57 reticulate_1.16
## [57] dqrng_0.2.1 mapproj_1.2.7
## [59] foreign_0.8-80 rsvd_1.0.3
## [61] FNN_1.1.3 ellipsis_0.3.1
## [63] farver_2.0.3 pkgconfig_2.0.3
## [65] locfit_1.5-9.4 labeling_0.3
## [67] tidyselect_1.1.0 rlang_0.4.9
## [69] munsell_0.5.0 cellranger_1.1.0
## [71] tools_4.0.2 generics_0.0.2
## [73] statnet.common_4.3.0 broom_0.7.2
## [75] evaluate_0.14 yaml_2.2.1
## [77] knitr_1.30 tidygraph_1.2.0
## [79] zip_2.0.4 purrr_0.3.4
## [81] dendextend_1.13.4 pbapply_1.4-2
## [83] future_1.17.0 compiler_4.0.2
## [85] beeswarm_0.2.3 curl_4.3
## [87] png_0.1-7 ggsignif_0.6.0
## [89] tweenr_1.0.1 tibble_3.0.4
## [91] statmod_1.4.34 stringi_1.4.6
## [93] RSpectra_0.16-0 gdtools_0.2.2
## [95] forcats_0.5.0 lattice_0.20-41
## [97] Matrix_1.2-18 vctrs_0.3.5
## [99] pillar_1.4.4 lifecycle_0.2.0
## [101] GlobalOptions_0.1.2 BiocNeighbors_1.6.0
## [103] data.table_1.12.8 cowplot_1.0.0
## [105] bitops_1.0-6 irlba_2.3.3
## [107] R6_2.4.1 network_1.16.0
## [109] rio_0.5.16 vipor_0.4.5
## [111] codetools_0.2-16 dichromat_2.0-0
## [113] MASS_7.3-51.6 assertthat_0.2.1
## [115] pkgmaker_0.31.1 rjson_0.2.20
## [117] withr_2.2.0 GenomeInfoDbData_1.2.3
## [119] hms_0.5.3 grid_4.0.2
## [121] coda_0.19-3 tidyr_1.1.2
## [123] rmarkdown_2.4 DelayedMatrixStats_1.10.0
## [125] carData_3.0-4 ggforce_0.3.1
## [127] ggbeeswarm_0.6.0